Self-E: Smartphone-Supported Guidance for Customizable Self-Experimentation

The ubiquity of self-tracking devices and smartphone apps has empowered people to collect data about themselves and try to selfimprove. However, people with little to no personal analytics experience may not be able to analyze data or run experiments on their own (self-experiments). To lower the barrier to intervention-based self-experimentation, we developed an app called Self-E, which guides users through the experiment. We conducted a 2-week diary study with 16 participants from the local population and a second study with a more advanced group of users to investigate how they perceive and carry out self-experiments with the help of Self-E, and what challenges they face. We find that users are influenced by their preconceived notions of how healthy a given behavior is, making it difficult to follow Self-E's directions and trusting its results. We present suggestions to overcome this challenge, such as by incorporating empathy and scaffolding in the system.

• Human-centered computing → Empirical studies in HCI; Empirical studies in ubiquitous and mobile computing.